Integrating Manufacturing Process Planning with Scheduling via Operation-Based Time-Extended Negotiation Protocols

Abstract

Traditionally the process planning and scheduling functions are executed in a sequential way, and alternative resources are not usually considered. In this paper it is proposed the on-line adaptation of the process plan with alternatives, through the application of a feature-based time-extended negotiation protocol for decision-making about real-time routing of job orders of parts composed of machining features in a job-shop environment. The protocol is modified from the contract net protocol to cater for the multiple-task and many-to-many negotiations. The grouping of the machining features enables gains in the setup times, and these earnings are associated with the lower number of setups usually caused by unnecessary machines changes. The proposed architecture comprises the following types of agents: part, resource, server, failure, order, and synchronizer agents. For each part, all feasible routings are considered as alternative process plans provided the different manufacturing times are in each machine are taken into account. The time-extended negotiation period, which allows the visualization of all of the times involved in the manufacture of each part, including those times that are not considered in systems of this nature, such as the negotiation times among agents. Through the inclusion of a failure agent it is possible to verify the robustness of the system and to measure the impact caused by production disturbances, such as machine breakdowns. A dynamic Gantt chart is generated starting with each of the simulations, having as input the changes of status of the system, such as the beginning and end of processes, as well as production disturbances. The proposed system is already being used, and extensive experiments have been conducted and the performance measures, including routings, makespan and flowtime, are compared with those obtained by the following approaches found in the literature: traditional hierarchical approach, an existing cooperative co-evolutionary algorithm, and the search technique base on the co-evolutionary algorithm.Pages: 319-32